The field of the invention is systems and methods for quantitative ultrasound (“QUS”). More particularly, the invention relates to systems and methods for classifying and characterizing tissues as being associated with a particular histological state using QUS.
Clinical ultrasound is a useful, and noninvasive tool for diagnosing cancer and other diseases. In addition to ultrasound's ability to non-invasively differentiate malignant tumors from their benign counterpart, the ability to characterize a malignant tumor in terms of its histological grade is of paramount importance for staging and treatment design. Due to the many instrument parameters that can be chosen during an ultrasound imaging session, however, a comparative interpretation of conventional B-mode images becomes difficult when different ultrasound machines are used, or when different settings are applied. Additionally, B-mode images lack information about microstructural properties of soft tissues.
Quantitative ultrasound (“QUS”) techniques, which examine the frequency-dependent backscatter of tissues independent of the instrument settings, have been suggested to overcome this limitation. Such techniques have been applied in-vivo to reveal information about the tissue's underlying microstructure, or histological state, enabling the differentiation of disease from non-disease, the differentiation of viable from apoptotic tissue, and the characterization of a disease into its subtypes. Specifically, parameters including effective scatterer diameter (“ESD”) and effective acoustic concentration (“EAC”) have demonstrated the potential to distinguish between mouse tumor models of mammary carcinoma and fibroadenoma. However, the estimation of effective scatterer size and acoustic concentration require prior knowledge about the backscattering model, which can often be complicated to characterize in the case of tissue particular tissues.
To avoid complex model fitting, basic spectral parameters extracted via a linear regression analysis of the radio frequency (“RF”) echo signal spectrum, including mid-band fit (“MBF”), spectral slope (“SS”), and spectral 0-MHz intercept (“SI”), were proposed for tissue characterization. Such quantitative parameters have been previously used to characterize various types of tissue abnormalities, including those in prostate, lymph nodes, and myocardium, and to detect apoptotic cell death. By modeling the ultrasonic power spectrum as an acoustic impedance autocorrelation function, it has been demonstrated that SS can be related to effective scatterer size, SI can be related to acoustic concentration, and MBF can be related to both effective scatterer size and acoustic concentration. Alternatively, scatterer spacing, also known as spacing among scatterers (“SAS”), has been investigated as a tissue characterizing parameter when the tissue of interest contained detectable periodicity in its structural organization. In this context, scatterer spacing has been applied to characterize human breast tumors by categorizing them into normal, fibroadenoma, simple carcinoma, or infiltrating papillary carcinoma. Other studies have also investigated the potential of SAS for characterizing diffuse diseases of the liver.
While the conventional quantitative ultrasound mean parameters discussed above describe the frequency-dependent properties of tissue microstructure, textural characteristics of their parametric maps can provide second-order statistics by quantifying the patterns of gray-level transitions. A number of previous studies have applied the textural features of ultrasound B-mode images to distinguish between malignant and benign breast tumors. The principle behind this tissue classification technique is that malignant tumors tend to present as heterogeneous internal echoes, while benign tumors often demonstrate homogeneous internal echoes. Textural analysis techniques aim at extracting the tissue internal echo properties or “texture,” based on the ultrasonic gray-level transitions, and hence can define differentiable characteristics in this application. However, conventional B-mode images may also present undesirable variations in textural estimates due to variations in instrument settings, ultrasound beam diffraction, and attenuation effects.
It would therefore be desirable to provide systems and methods for classifying tissues as being associated with particular histological states using ultrasound, but without the limitations in accuracy that are associated with analyzing B-mode images. Advantageously, such systems and methods would be capable of classifying tissues based on histological states including both general classifications (e.g., normal, cancerous) and subtype classifications (e.g., tumor grade, liver fibrosis stage).